Pictorial images are often classified by the particular event, subject, or the like for convenience of retrieving, reviewing, and albuming of the images. This classification is usually achieved by either manually or automatically segmenting the images into appropriate groups. A manual method would involve visually inspecting each image and then placing the image into the appropriate group. An automated albuming method would typically group the images in some automatic manner by color, shape or texture in order to partition the images into groups of similar image characteristics.
Although the presently known and utilized methods for partitioning images are satisfactory, there are drawbacks. The manual classification method is obviously time consuming, and the automated albuming method, although theoretically classifying the images into events, is susceptible to misclassification due to the inherent inaccuracies involved with classification by color, shape or texture. In either method, when two pictures are identified as duplicates, one of them is typically removed and will not appear in the resulting album. Since a consumer will not be satisfied if the automatic albuming process removes a picture that should have been in the album, the precision of the duplicate detection algorithm has to be high. Consequently, a need exists for overcoming the above-described drawbacks.